基于改进PointPillars的激光雷达三维目标检测

Laser Radar 3D Target Detection Based on Improved PointPillars

针对目前基于点云的三维目标检测算法中小目标检测效果差的问题,提出了基于改进PointPillars模型的三维目标检测方法。首先,改进了PointPillars模型中的pillar特征网络,提出了一个新的pillar编码模块,在编码网络中引入了平均池化和注意力池化,充分考虑了每个pillar模块的局部详细几何信息,提高了每个pillar模块的特征表示能力,从而提升了模型的小目标检测性能。其次,基于ConvNeXt改进了骨干网络中的二维卷积下采样模块,使模型在网络特征提取阶段能够提取丰富的上下文语义信息和全局特征,从而增强了算法的特征提取能力。在公开数据集KITTI上进行验证,实验结果表明,所提方法具有更高的检测精度,相较于原网络,改进后的算法的平均检测精度提升了3.63个百分点,证明了该方法的有效性。

A 3D object detection method based on improved PointPillars model is proposed to address the problem of poor detection performance of small objects in current point cloud based 3D object detection algorithms.First,the pillar feature network in the PointPillars model is improved,and a new pillar encoding module is proposed.Average pooling and attention pooling are introduced into the encoding network,fully considering the local detailed geometric information of each pillar module,which improve the feature representation ability of each pillar module and further improve the detection performance of the model on small targets.Second,based on ConvNeXt,the 2D convolution downsampling module in the backbone network is improved to enable the model extract rich context semantic information and global features during feature extraction process,thus enhancing the feature extraction ability of the algorithm.The experimental results on the public dataset KITTI show that the proposed method has higher detection accuracy.Compared with the original network,the improved algorithm has an average detection accuracy improvement of 3.63 percentage points,proving the effectiveness of the method.